1,372 research outputs found
Botulinum toxin for the treatment of lower limb cramp pain in patients with Amyotrophic Lateral Sclerosis
Background:
Muscle cramps and pain associated with them can be seen in patients with amyotrophic lateral sclerosis (ALS) and are known to reduce the quality of life. Pharmacological treatment may not benefit all patients in treating these cramps. We assess the efficacy of Onabotulinum toxin A (BTX-A) in the treatment of lower limb cramps in patients with ALS.
Methods:
This retrospective chart review included a total of ten patients with ALS who suffered from pain due to lower limb cramps and were managed with BTX-A. Data including patient demographics, visual analog pain scale at different intervals during follow up, ALS functional rating scale and site of onset of ALS symptoms were documented. The pain score at baseline (before administration), at 3 months follow up and at 6 months follow up were compared using Wilcoxon test to assess BTX-A’s efficacy.
Results:
A significant improvement in average pain score due to cramps from baseline to the 6-month interval with a change of 3.1±0.7 (p<0.05,95%CI) was seen on the pain scale. No adverse events were noted during administration or post injections.
Conclusion:
Local BTX-A administration is an efficacious and safe procedure for improving pain associated with cramps in patients with ALS
Clinical Experience of Edaravone in Amyotrophic Lateral Sclerosis
Objective: To describe clinical experience with edaravone in ALS over a period of 12 months
Methods:The current study retrospectively investigated characteristics in a group of patients (n=31) with ALS who underwent edaravone treatment. Information including age, gender, race, and site of onset of symptoms were collected for all patients. Adverse events with edaravone therapy was documented where available.
Results:The average age of the patients observed was 62.09 years, with 18 males and 13 females. 18 patients had limb onset, 12 bulbar onset, and 1 diaphragmatic onset. 7 of the 31 patients discontinued treatment at the end of one year. The average age of patients who discontinued edaravone was 65.71 years, of whom which 3 had limb onset, 3 bulbar onset, and 1 diaphragmatic onset. No perceived benefit, port complications, systemic bacteremia, and development of atrial fibrillation were documented as reasons for discontinuation of therapy.Conclusion: Edaravone is well tolerated in ALS patients at the end of one year. Lack of perceived benefit and port related complications are common reasons for discontinuation of treatmen
A COMMUNITY BASED CROSS-SECTIONAL STUDY ON MORBIDITY PATTERN OF ELDERLY IN RURAL AREA OF JHALAWAR, RAJASTHAN
Objectives: The aim of the study was to study the morbidity pattern and burden among the elderly residing in the field practice area of Rural Health Training Centre Mandawar. The aim of the study was to identify the various factors associated with morbidity among the study population. Assessment of the morbidity profile will help in the application of interventions, to improve the health status and the quality of life of the elderly.
Methods: Therefore, a total of 880 elderly were selected. Multiple house visits were done and data were collected by interviewer method, observation, and clinical examination of the study population. Diagnosis of the disease was made on the basis of history, investigations, clinical examination, and treatment report. Statistical analysis was done using Statistical Package for the Social Sciences 25 trial version.
Results: Out of 880 elderly, 83.9% belonged to the age group of 60–74 years, the majority (87.4%) were found to be Hindus, 44% lived in three generation families. The most of the elderly were illiterate (46.7%) and the majority (44.2%) belonged to class IV socio-economic status. The morbidity load was 2370. The average morbidity per person was 1.56. The majority of the elderly (32%) had two morbidities. Visual impairment was the most common morbidity and it was more common in males. The association of morbidity was found to be statistically significant with gender, age category, and financial status.
Conclusion: The present study revealed that the prevalence of morbidity among the elderly is very high
Efficacy of botulinum toxin for treating cramps in peripheral neuropathy
Abstract
Introduction:
Muscle cramps are a common occurrence in patients with peripheral neuropathy and are known to cause significant distress and decrease the quality of life. Although several drug formulations have been used in the management of cramps, there is significant variability in terms of efficacy and tolerability in patients with peripheral neuropathy. This study aims to assess the efficacy of botulinum toxin A in the management of lower limb cramps in patients with peripheral neuropathy.
Methods:
This retrospective chart review included a total of ten patients with peripheral neuropathy with cramps. Relevant data such as age, gender, race, pain score and cause of peripheral neuropathy were documented. Statistical analyses to compare the variables was done using the Wilcoxon Test. The pain score before the administration, at 3-month, 6 month and 9 months follow up were compared.
Results
All patients enrolled in the study showed improvement of pain assessed by visual pain analog scale. An improvement of 1.60 (95%CI, p<0.05), 2.70 (95%CI, p<0.05) and 3.50 (95%CI, p=0.05) was noted between test scores from before administration of botulinum toxin to 3-month, 6 months and 9 months follow up with a range of 6, 4 and 4 respectively.
Conclusion:
Local BTX-A infiltration is a likely efficacious and safe procedure for improving pain associated with cramps in patients with peripheral neuropathy
Acute Hepatitis B and Acute HIV Coinfection in an Adult Patient: A Rare Case Report
Acute HIV and acute hepatitis B coinfection is extremely rare. A 23-year-old homosexual man was admitted to our hospital with 5-day history of fever, malaise, and back pain with initial laboratory values showing severe transaminitis. The clinical picture was initially suggestive of acute viral hepatitis, which on further testing revealed acute hepatitis B and acute HIV coinfection. Although the patient was asymptomatic, a decision was made to start antiretroviral therapy. At 2-month followup, liver function tests were normal with undetectable viral loads. The early treatment of acute HIV/HBV coinfections likely contributed to eventual seroconversion with immunity to HBV in a severely immunocompromised host. To the best of our knowledge, this is the first case report of acute Hepatitis B and acute HIV coinfection and its management. In conclusion, early treatment of acute hepatitis B in immunocompromised patients may be beneficial
QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results
Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder translating DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties could enable clinical review of the most uncertain regions, thereby building trust and paving the way toward clinical translation. Several uncertainty estimation methods have recently been introduced for DL medical image segmentation tasks. Developing scores to evaluate and compare the performance of uncertainty measures will assist the end-user in making more informed decisions. In this study, we explore and evaluate a score developed during the BraTS 2019 and BraTS 2020 task on uncertainty quantification (QU-BraTS) and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation. This score (1) rewards uncertainty estimates that produce high confidence in correct assertions and those that assign low confidence levels at incorrect assertions, and (2) penalizes uncertainty measures that lead to a higher percentage of under-confident correct assertions. We further benchmark the segmentation uncertainties generated by 14 independent participating teams of QU-BraTS 2020, all of which also participated in the main BraTS segmentation task. Overall, our findings confirm the importance and complementary value that uncertainty estimates provide to segmentation algorithms, highlighting the need for uncertainty quantification in medical image analyses. Finally, in favor of transparency and reproducibility, our evaluation code is made publicly available at: this https URL
Information Gain Sampling for Active Learning in Medical Image Classification
Large, annotated datasets are not widely available in medical image analysis
due to the prohibitive time, costs, and challenges associated with labelling
large datasets. Unlabelled datasets are easier to obtain, and in many contexts,
it would be feasible for an expert to provide labels for a small subset of
images. This work presents an information-theoretic active learning framework
that guides the optimal selection of images from the unlabelled pool to be
labeled based on maximizing the expected information gain (EIG) on an
evaluation dataset. Experiments are performed on two different medical image
classification datasets: multi-class diabetic retinopathy disease scale
classification and multi-class skin lesion classification. Results indicate
that by adapting EIG to account for class-imbalances, our proposed Adapted
Expected Information Gain (AEIG) outperforms several popular baselines
including the diversity based CoreSet and uncertainty based maximum entropy
sampling. Specifically, AEIG achieves ~95% of overall performance with only 19%
of the training data, while other active learning approaches require around
25%. We show that, by careful design choices, our model can be integrated into
existing deep learning classifiers.Comment: Paper accepted at UNSURE 2022 workshop at MICCAI 202
The convergence of budding reckoning Soft computing technologies
Soft Computing methodologies represents the amalgamation of promising problem - solving technologies such as Neural Networks (NNs), Fuzzy Logic (FL), Probabilistic Reasoning (PR) and Genetic Algorithms (GAs) . We present a collection of tools and methods that can be used to perform diagnostics, estimation, and control. These tools are an enormous match for real - world applications that are characterized by imprecise, uncertain data and incomplete domain knowledge. Each of these technologies offer us with complementary reasoning and sear ching methods to solve complex problems. In this paper after a brief description of each of these technologies, we will analyze some of their most useful combinations, such as the use of FL to control GAs and NNs parameters; the application of GAs to evolv e NNs or to tune FL controllers; and the implementation of FL controllers as NNs tuned by back propagation - type algorithms . We summarize the recompense of SC techniques and in particular the synergy derived from the use of hybrid SC system
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